In the cloud environment, it brings better reliability and robustness with geographically distributed datacenters. As the growth of large-scale applications in geo-distributed cloud systems, the resource demand from different areas increases violently, and researchers pay more attention to meet as many cloud users' VM demands as possible by using limited cloud resources. However, there exist many issues for cloud users in existing works, such as the VM demands being refused and high response latency. In this paper, we present a cloud system model for the cloud provider to dynamically expand the scale of geo-distributed date centers. In our model, the cloud provider rents hardware resources from other resource owners (ROs), who have redundant resources and are willing to lease them. Since the ROs possess vast resources and spread all over the global, our system model can deploy more cloud users' VMs and effectively reduce the bandwidth cost. We propose an optimization problem for the cloud provider to maximize the profit, and carefully solve it in different conditions. Our simulation results show that our system model and algorithms can effectively improve the user satisfaction and the total revenue and reduce the average latency of users' requests.

Cooperative communication (CC) is shown to be a promising technology to improve the spatial diversity without additional equipments and antennas. The choice of relay nodes can significantly affect the performance of wireless cooperative networks, e.g. energy- efficiency. Most of existing works focused on dedicating one relay node to a transmission pair in the network. However, the energy-efficiency can be improved by sharing each relay node among more than one transmission pair. In this paper, we first introduce the feasibility of shared relay assignment to improve the energy-efficiency while achieving the required bandwidth for each transmission pair. Then, the Minimum Energy Consumption (MEC) problem of CC with shared relay is defined, and we formulate it as a mixed integer optimization program and prove it is NP-hard. A Simulated Annealing (SA) algorithm is designed to solve this problem. We conduct abundant simulation experiments to evaluate the performance of our proposed algorithm. The simulation results show that the SA algorithm can save about 15 energy consumption on average than the previous work in wireless networks.

To explore the advantages of software defined network (SDN), while preserving the legacy networking systems, a natural deployment strategy is to deploy a hybrid SDN incrementally to improve the network performance. In this paper, we address two technical challenges: an incremental deployment strategy and a throughput-maximization routing, for deploying a hybrid network incrementally. For incremental deployment, we propose a heuristic algorithm for deploying a hybrid SDN under the budget constraint, and prove the approximate factor of 1- 1/e. For throughput-maximization routing, we apply a depth-first-search method and a randomized rounding mechanism to solve the multi-commodity h-splittable flow routing problem in a hybrid SDN, where h ≥ 1. We also prove that our method has approximation ratio O(1/log N), where N is the number of links in a hybrid SDN. We then show, by both analysis and simulations, that our algorithms can obtain significant performance gains and perform better than the theoretical worst-case bound. For example, our incremental deployment scheme helps to enhance the throughout about 40% compared with the previous deployment scheme by deploying a small number of SDN devices, and the proposed routing algorithm can improve the throughput about 31% compared with ECMP in hybrid networks.

As more and more applications migrate into clouds, the placement of virtual machines for these applications has much impact on the performance of cloud systems. A number of virtual machine (VM) placement techniques have been proposed over recent years. However, most of the existing works on VM placement ignore the response latency of the requests from tenants. In this paper, we investigate the techniques of VM placement with stochastic requests from the tenants to minimize the total (average) response latency. We first model the requests for each application from the corresponding tenant as independent Poisson stream. Moreover, the VMs are modeled as simple M/M/1 queueing systems. Then, we define the problem of VM placement for minimizing the total response delay (VMMD) and show it is NP-hard. We propose three heuristic algorithms, namely, Greedy, Local Adjustment (LA) and Simulated Annealing (SA). We conduct abundant simulation experiments to evaluate the performance of our proposed algorithms. The simulation results show that the proposed algorithms are efficient in decreasing the total response latency of the requests from tenants. Especially, the SA heuristic, which decreases the total response latency about 68% at most, shows the best performance on minimizing the total response latency in cloud systems.

The choice of relay nodes significantly affects the performance of wireless cooperative networks. Previous research mostly focused on dedicating one relay node to a source node in the network. However, fairness can be improved by sharing each relay node among more than one source node. This paper first defines the shared relay assignment for (max-min) fairness (SRAF) problem, and formalizes it using a mixed integer program. We then propose a heuristic algorithm (RRA) to solve this problem. The algorithm mainly uses the binary search and rounding mechanisms to implement the shared relay assignment, so that the minimum throughput of all source nodes is improved. The theoretical analysis proves that the proposed algorithm can reach the approximate performance of 2+ε, where ε is an arbitrarily small positive number. An improved version of RRA, called IRRA, can improve the minimum throughput while still preserving the worst-case performance. Our simulations show that the IRRA algorithm can achieve about 18% improvement over the best existing approach in the minimum throughout among the source nodes.